terum.ai enables engineering teams to identify, evaluate, and share the most effective AI agent skills across their organization. The platform runs A/B tests comparing agent performance with and without candidate skills in real production environments, generating auditable receipts with cost, time, and quality metrics. Built around automated eval generation and continuous monitoring, it helps detect skill degradation as models evolve over time.
Funding
Funding not disclosed
Founders
Product
Problem
Engineering organizations struggle to capture and distribute the tacit knowledge behind their best AI agents. High-performing engineers often develop effective custom skills and workflows that remain siloed, leaving less experienced team members to rely on generic model behavior that is slower, costlier, and prone to errors.
Solution
terum.ai provides a framework for evaluating, publishing, and monitoring AI agent skills so teams can systematically share what works. The platform runs head-to-head A/B tests—candidate skill versus baseline—in fresh sandboxes on the user's own machine, generating deterministic pass/fail verdicts backed by cost, time, and quality comparisons. It creates personalized test cases that emulate the team's production environment Zhu and produces auditable receipts for every evaluation run. Governance controls, security scanning, and multi-version comparison allow teams to confidently distribute skills at scale, while continuous monitoring flags when model updates degrade a skill's effectiveness.
Target Audience
Primary users include engineering leaders and platform teams at software companies deploying AI coding agents who want to raise baseline performance across all engineers. It also serves teams with formal governance needs around skill review, security, and audit compliance.
Features
- A/B test framework that runs the same tasks with and without a skill in isolated sandboxes, producing net-lift verdicts (PASS/NEUTRAL/FAIL) based on deterministic checks and judge-based transcript comparisons
- Automated test case generation that creates personalized eval cases from the team's production workflow when no authored evals exist
- Audit receipts recording every publish, install, eval, and approval with cryptographic digests of the exact bytes evaluated
- Security scanning of every skill version for risky tool grants, shell commands, network calls, and secrets before installation
- Governance controls permitting admins to set review requirements, tool access limits, and eval pass thresholds for publishing
- Continuous degradation monitoring that alerts teams when model updates impact skill performance, with three-arm comparison (baseline, candidate, incumbent) for teams